System and Method for Estimating Forward Retail Commodity Price Within a Geographic Boundary

ABSTRACT

Embodiments disclosed herein provide a new way to generate estimated forward retail prices for a retail commodity within a geographic boundary that represents a target market. Using estimates for local retail prices, combined with knowledge of current and historical wholesale prices, embodiments disclosed herein enable the creation of a forward estimate of retail prices on fuels for a specific location, time period, and fuel grade. In some embodiment, the process of creating a forward estimate of retail prices on fuels comprises performing a predictive modeling utilizing wholesale gasoline prices, rack markup, retail markup, and taxes on a location, time period, and fuel grade basis. In some cases, the estimated forward retail prices thus generated can be used in a pricing model for price protection services for that retail commodity in that target market.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims a benefit of priorityunder 35 U.S.C. 120 of the filing date of U.S. patent application Ser.No. 12/030,086, filed Feb. 12, 2008, entitled “SYSTEM AND METHOD FORESTIMATING FORWARD RETAIL COMMODITY PRICE WITHIN A GEOGRAPHIC BOUNDARY”,which in turn claims the benefit of priority under 35 U.S.C. §119 toProvisional Patent Application No. 60/900,931, filed Feb. 12, 2007,entitled “SYSTEM AND METHOD FOR ESTIMATING FORWARD RETAIL COMMODITYPRICE,” Provisional Patent Application No. 60/900,930, filed Feb. 12,2007, entitled “SYSTEM AND METHOD OF DETERMINING A RETAIL COMMODITYPRICE WITHIN A GEOGRAPHIC BOUNDARY,” and Provisional Patent ApplicationNo. 60/966,566, filed Aug. 29, 2007, entitled “SYSTEM AND METHOD OFDETERMINING A RETAIL COMMODITY PRICE WITHIN A GEOGRAPHIC BOUNDARY,” theentire contents of which are hereby expressly incorporated by referencefor all purposes.

FIELD OF THE INVENTION

The present invention relates generally to predictive modeling on retailcommodity prices. More particularly, the present invention relates to asystem and method for determining forward retail commodity prices withparticular constraints including time and geographic boundaries.

BACKGROUND

There is a continuing interest in the relationship between the retailprice and the wholesale price for commodities. The retail price of acommodity generally reflects the wholesale price plus the cost ofmarketing the commodity from the raw material level to the retail level.In the case of gasoline, the Energy Information Administration (EIA),the independent statistical and analytical agency within the Departmentof Energy (DOE), published a study entitled “Price Changes in theGasoline Market,” DOE/EIA-0626, February 1999. In this EIA report, theretail price of gasoline is divided into three main components: crudeoil prices, manufacturing and marketing costs and profits, and taxes(see FIG. 1). Crude oil prices are considered to be the most importantcomponent of the retail price of gasoline. One reason is that, whilemanufacturing and marketing costs may change relatively slowly, crudeoil prices can be extremely volatile, even on a daily basis. Thisvolatility makes accurate prediction of the retail price of the gasolinevery difficult. At the refinery level, the spot gasoline prices may movesymmetrically in response to crude oil changes, which means that thevolatility is also evident at the refinery level (see FIG. 2). Theexistence of a futures market for domestic crude oil permits theknowledge of actual prices to be disseminated very quickly. This allowsgasoline prices further down the distribution chain such as rack pricesto be closely correlated to spot prices (see FIG. 3).

However, at the retail level, previous studies on gasoline prices havefound price asymmetry. Price asymmetry is the phenomenon where pricestend to move differently depending on their direction. In the case ofgasoline, this means that retail prices for gasoline may rise fasterthan they fall. Moreover, although retail prices are not very closelycorrelated to rack prices (see FIG. 4), for the most part they move inresponse to changes in wholesale or raw material prices further upstreamin the manufacturing-distribution chain. At the national level, laggedwholesale prices alone can thus be used to predict retail prices. FIG. 5shows an example of a national average of actual and forecasted retailprices for gasoline on the national level where the forecasted retailprices are calculated using lagged wholesale prices on a week-by-weekbasis. Specifically, the next week's change in retail gasoline prices onthe national level can be predicted from a symmetrical response modelwhich consists of a moving average of prior wholesale gasoline prices.The closeness of the predicted estimates to the actual values showsthat, on the national level, wholesale and retail gasoline prices aregenerally very closely linked. This closeness helps explain why itremains very difficult to predict price movement of gasoline, a type ofcommodity, below the national level and/or at a particular locale.

SUMMARY OF THE INVENTION

Within this disclosure, the term “commodity” refers to an article ofcommerce—an item that can be bought and sold freely on a market. It maybe a product which trades on a commodity exchange or spot market andwhich may fall into one of several categories, including energy, food,grains, and metals. Currently, commodities that can be traded on acommodity exchange include, but are not limited to, crude oil, lightcrude oil, natural gas, heating oil, gasoline, propane, ethanol,electricity, uranium, lean hogs, pork bellies, live cattle, feedercattle, wheat, corn, soybeans, oats, rice, cocoa, coffee, cotton, sugar,gold, silver, platinum, copper, lead, zinc, tin, aluminum, titanium,nickel, steel, rubber, wool, polypropylene, and so on. Note that acommodity can refer to tangible things as well as more ephemeralproducts. Foreign currencies and financial indexes are examples of thelatter. For example, positions in the Goldman Sachs Commodity Index(GSCI) and the Reuters Jefferies Consumer Research Board Index (RJCRBIndex) can be traded as a commodity. What matters is that something beexchanged for the thing. New York Mercantile Exchange (NYMEX) andChicago Mercantile Exchange (CME) are examples of a commodity exchange.Other commodities exchanges also exist and are known to those skilled inthe art.

In a simplified sense, commodities are goods or products with relativehomogeneousness that have value and that are produced in largequantities by many different producers; the goods or products from eachdifferent producer are considered equivalent. Commoditization occurs asa goods or products market loses differentiation across its supply base.As such, items that used to carry premium margins for marketparticipants have become commodities, of which crude oil is an example.However, a commodity generally has a definable quality or meets astandard so that all parties trading in the market will know what isbeing traded. In the case of crude oil, each of the hundreds of gradesof fuel oil may be defined. For example, West Texas Intermediate (WTI),North Sea Brent Crude, etc. refer to grades of crude oil that meetselected standards such as sulfur content, specific gravity, etc., sothat all parties involved in trading crude oil know the qualities of thecrude oil being traded. Motor fuels such as gasoline represent examplesof energy-related commodities that may meet standardized definitions.Thus, gasoline with an octane grade of 87 may be a commodity andgasoline with an octane grade of 93 may also be a commodity, and theymay demand different prices because the two are not identical—eventhough they may be related. Those skilled in the art will appreciatethat other commodities may have other ways to define a quality. Otherenergy-related commodities that may have a definable quality or thatmeet a standard include, but are not limited to, diesel fuel, heatingoils, aviation fuel, and emission credits. Diesel fuels may generally beclassified according to seven grades based in part on sulfur content,emission credits may be classified based on sulfur or carbon content,etc.

Historically, risk is the reason exchange trading of commodities began.For example, because a farmer does not know what the selling price willbe for his crop, he risks the margin between the cost of producing thecrop and the price he achieves in the market. In some cases, investorscan buy or sell commodities in bulk through futures contracts. The priceof a commodity is subject to supply and demand.

A commodity may refer to a retail commodity that can be purchased by aconsuming public and not necessarily the wholesale market only. Oneskilled in the art will recognize that embodiments disclosed herein mayprovide means and mechanisms through which commodities that currentlycan only be traded on the wholesale level may be made available toretail level for retail consumption by the public. One way to achievethis is to bring technologies that were once the private reserves of themajor trading houses and global energy firms down to the consumer leveland provide tools that are applicable and useful to the retail consumerso they can mitigate and/or manage their measurable risks involved inbuying/selling their commodities. One example of an energy relatedretail commodity is motor fuels, which may include various grades ofgasoline. For example, motor fuels may include 87 octane grade gasoline,93 octane grade gasoline, etc as well as various grades of diesel fuels.Other examples of an energy related retail commodity could be jet fuel,heating oils, electricity or emission credits such as carbon offsets.Other retail commodities are possible and/or anticipated.

While a retail commodity and a wholesale commodity may refer to the sameunderlying good, they are associated with risks that can be measured andhandled differently. One reason is that, while wholesale commoditiesgenerally involve sales of large quantities, retail commodities mayinvolve much smaller transaction volumes and relate much more closely tohow and where a good is consumed. The risks associated with a retailcommodity therefore may be affected by local supply and demand andperhaps different factors. Within the context of this disclosure, thereis a definable relationship between a retail commodity and the exposureof risks to the consumer. This retail level of the exposure of risks maycorrelate to the size and the specificity of the transaction in whichthe retail commodity is traded. Other factors may include thegranularity of the geographic market where the transaction takes place,and so on. For example, the demand for heating oil No. 2 in January maybe significantly different in the Boston market than in the Miamimarket.

Pricing a retail commodity can be a very difficult process, particularlyif that retail commodity tends to fluctuate in an unpredictable manner.Take gasoline as an example, as the price of oil continues to fluctuateglobally and fluidly, fuel prices at the pump can change from locationto location on a daily or even hourly basis. In such a volatile market,it is extremely difficult to reliably predict retail fuel prices for aspecific location, time period, and fuel grade/type.

Embodiments disclosed herein provide a new way to generate estimatedforward retail gas prices within a geographic boundary based on aplurality of factors. More specifically, some embodiments disclosedherein provide a system and method for determining an estimated forwardretail price (EFRP) for gasoline based on location (L), time period (T),and fuel grade (G) within a geographic boundary that represents a targetmarket.

Using estimates for local retail prices, combined with knowledge ofcurrent and historical wholesale prices, embodiments disclosed hereinenable the creation of a forward estimate of retail prices on fuels fora specific location, time period, and fuel grade. Data generated in theprocess can be used as the primary input to an analytic process thatsupports a fuel purchase management and decision making tool, benefitingits variety of users. Examples of such users include, but not limitedto, fleet managers, financial managers, consumers, investment bankers,and other financial analysts, etc. In some embodiment, the process ofcreating a forward estimate of retail prices on fuels comprisesperforming a predictive modeling utilizing wholesale gasoline prices,rack markup, retail markup, and taxes on a location, time period, andfuel grade basis. The predictive model disclosed herein can be augmentedin various ways. As an example, one embodiment of the predictive modelcan be configured to take into consideration the effect oflow-probability events on the future prices of gasoline. Examples ofsuch events may include, but not limited to, natural or manmade eventssuch as hurricanes, fire, war, etc.

These, and other, aspects will be better appreciated and understood whenconsidered in conjunction with the following description and theaccompanying drawings. The following description, while indicatingvarious embodiments and numerous specific details thereof, is given byway of illustration and not of limitation. Many substitutions,modifications, additions or rearrangements may be made within the scopeof the disclosure, and the disclosure includes all such substitutions,modifications, additions or rearrangements.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the disclosure and the advantagesthereof may be acquired by referring to the following description, takenin conjunction with the accompanying drawings in which like referencenumbers generally indicate like features and wherein:

FIG. 1 depicts a plot diagram showing one way of dividing the retailprice of gasoline into three components.

FIG. 2 depicts a plot diagram showing the historical relationshipbetween crude oil prices and spot gasoline prices.

FIG. 3 depicts a plot diagram showing the historical relationshipbetween spot gasoline prices and rack prices.

FIG. 4 depicts a plot diagram showing the historical relationshipbetween rack prices and retail prices.

FIG. 5 depicts a plot diagram showing actual and forecasted retailprices for gasoline on a weekly basis at the national level.

FIG. 6 depicts a simplified block diagram showing components of theforward retail price for a retail commodity according to one embodiment.

FIG. 7 is a flow diagram depicting one example embodiment of a methodfor determining the current retail price of a commodity for a specificlocation (L), time period (T), and grade (G).

FIG. 8 is a plot diagram depicting exemplary NYMEX RBOB gasoline futureshistorical data.

FIG. 9 is a flow diagram depicting a method for synthetically creating aforward market curve of an expected retail price for gasoline for aperiod of time within a market having a geographic boundary.

FIG. 10 is an exemplary plot depicting the increase as well as decreaseof the estimated wholesale price over time.

DETAILED DESCRIPTION

The disclosure and the various features and advantageous details thereofare explained more fully with reference to the non-limiting embodimentsthat are illustrated in the accompanying drawings and detailed in thefollowing description. Descriptions of well known starting materials,processing techniques, components and equipment are omitted so as not tounnecessarily obscure the disclosure in detail. Skilled artisans shouldunderstand, however, that the detailed description and the specificexamples, while disclosing preferred embodiments, are given by way ofillustration only and not by way of limitation. Various substitutions,modifications, additions or rearrangements within the scope of theunderlying inventive concept(s) will become apparent to those skilled inthe art after reading this disclosure.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,product, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, process, article, orapparatus. Further, unless expressly stated to the contrary, “or” refersto an inclusive or and not to an exclusive or. For example, a conditionA or B is satisfied by any one of the following: A is true (or present)and B is false (or not present), A is false (or not present) and B istrue (or present), and both A and B are true (or present).

Additionally, any examples or illustrations given herein are not to beregarded in any way as restrictions on, limits to, or expressdefinitions of, any term or terms with which they are utilized. Insteadthese examples or illustrations are to be regarded as being describedwith respect to one particular embodiment and as illustrative only.Those of ordinary skill in the art will appreciate that any term orterms with which these examples or illustrations are utilized encompassother embodiments as well as implementations and adaptations thereofwhich may or may not be given therewith or elsewhere in thespecification and all such embodiments are intended to be includedwithin the scope of that term or terms. Language designating suchnonlimiting examples and illustrations includes, but is not limited to:“for example,” “for instance,” “e.g.,” “in one embodiment,” and thelike.

Before discussing specific embodiments, an exemplary hardwarearchitecture for implementing certain embodiments is described.Specifically, one embodiment can include a computer communicativelycoupled to a network (e.g., the Internet). As is known to those skilledin the art, the computer can include a central processing unit (“CPU”),at least one read-only memory (“ROM”), at least one random access memory(“RAM”), at least one hard drive (“HD”), and one or more input/output(“I/O”) device(s). The I/O devices can include a keyboard, monitor,printer, electronic pointing device (e.g., mouse, trackball, stylist,etc.), or the like. In some embodiments, the computer has access to atleast one database.

ROM, RAM, and HD are computer memories for storing computer-executableinstructions executable by the CPU. Within this disclosure, the term“computer-readable medium” is not limited to ROM, RAM, and HD and caninclude any type of data storage medium that can be read by a processor.For example, a computer-readable medium may refer to a data cartridge, adata backup magnetic tape, a floppy diskette, a flash memory drive, anoptical data storage drive, a CD-ROM, ROM, RAM, HD, or the like.

The functionalities and processes described herein can be implemented insuitable computer-executable instructions. The computer-executableinstructions may be stored as software code components or modules on oneor more computer readable media. Examples of computer readable mediainclude, but are not limited to, non-volatile memories, volatilememories, DASD arrays, magnetic tapes, floppy diskettes, hard drives,optical storage devices, or any other appropriate computer-readablemedium or storage device, etc. In one exemplary embodiment of theinvention, the computer-executable instructions may include lines ofcompiled C++, Java, HTML, or any other programming or scripting code.

Additionally, the functions of the present disclosure may be implementedon one computer or shared/distributed among two or more computers in oracross a network. Communications between computers implementingembodiments of the invention can be accomplished using any electronic,optical, radio frequency signals, or other suitable methods and tools ofcommunication in compliance with known network protocols.

Attention is now directed to one exemplary process flow for determiningestimated future retail prices for a retail commodity over a time periodin a market defined by a geographic boundary. Within this disclosure, ageographic boundary may be defined as a city, a borough, a county, astate, a country, a region, a zip code, or other predetermined area, ormay be arbitrarily defined as a designated market area (DMA), or somecombination or division.

The retail price of a commodity can have many components. Take gasolineas an example, in one embodiment, the retail price for gasoline may bedivided into four components: the wholesale price component, the rackmarkup component, the retail markup component, and the taxes component.Although gasoline is used herein as a specific example of a retailcommodity, one skilled in the art will appreciate that embodimentsdisclosed herein may be implemented or otherwise adapted for virtuallyany commodities, including, but not limited to electricity, natural gas,ethanol, etc.

In the example shown in FIG. 6, the rack markup and the retail markupcomponents together represent the total markup price for gasoline.Although lagged wholesale gasoline prices may be used to forecast retailgasoline prices on a week-by-week basis at the national level, there isnot a direct and precisely predictable correlation between wholesaleprices and retail prices that can be used to predict forward retailgasoline prices below the national level and/or within a particularlocale, including, but not limited to, city, a borough, a county, astate, a country, a region, a zip code, a designated market area (DMA)or the like, and may include some combination or division thereof.

Embodiments disclosed herein provide the ability to predict, in a morereliable and accurate manner, forward retail prices of a retailcommodity over a period of time in a market having a geographicboundary. This ability can be advantageous to an entity that providesprice protection on that retail commodity to other entities as well asend consumers in that market. In some embodiments, the pricing modeldisclosed herein can be compared with historical fuel consumption andpotential hedging schemes to implement a comprehensive solution (alsoreferred to as the Pricelock system) for price protection on retailcommodities. For more detailed teachings on the Pricelock system,readers are directed to U.S. patent application Ser. No. 11/705,571,filed Feb. 12, 2007, entitled “METHOD AND SYSTEM FOR PROVIDING PRICEPROTECTION FOR COMMODITY PURCHASING THROUGH PRICE PROTECTION CONTRACTS,”which is incorporated herein as if set forth in full. As one skilled inthe art can appreciate, the estimated future retail fuel prices can beuseful in estimating future fuel costs and volatility as well aspotential benefits from a price cap created via the aforementionedPricelock system. Within this disclosure, the terms “gasoline”, “gas”,and “fuel” are used interchangeably.

As a specific example, suppose it is desired to provide price protectionservices against retail gasoline prices to customers located in Austin,Tex., U.S.A. In order to price such services competitively andprofitably, a price protection service provider would need to know whatthe projected future retail price for gas in Austin would be threemonths from now, six months from now, a year from now, and so on. Insome embodiments, such knowledge may be obtained by syntheticallycreating a forward market curve of an expected retail price for gasolinefor a year or some longer period. To create such a forward market curvefor projecting the future retail prices for gasoline in Austin, onewould need to first assess the aforementioned components (i.e., thewholesale price component, the rack markup component, the retail markupcomponents, and the tax component) of today's retail price for gasolinewith respect to Austin.

In some embodiments, the wholesale price component of the retail pricefor gasoline includes and closely correlates to the price of crude oil.In some embodiments, the wholesale price component of the retail pricefor gasoline on any particular day can be determined based on somefutures contract traded on the New York Mercantile Exchange (NYMEX) onthat day. Examples of suitable futures contracts may include, but notlimited to, unleaded gasoline futures contracts, reformulated gasolineblendstock for oxygenate blending (RBOB) futures contracts, or the like.

RBOB is an unfinished gasoline product that does not contain theoxygenate methyl tertiary butyl ether chemical (MTBE), which is bannedin many states for polluting groundwater. RBOB contracts are traded pergrade on the NYMEX on the wholesale basis in units of 1,000 barrels(42,000 gallons). They are based on delivery at petroleum productsterminals in the New York harbor, the major East Coast trading centerfor imports and domestic shipments from refineries in the New Yorkharbor area or from the Gulf Coast refining centers. RBOB conforms toindustry standards for reformulated regular gasoline blendstock forblending with 10% denatured fuel ethanol (92% purity) as listed by theColonial Pipeline for fungible F grade for sales in New York and NewJersey. As a specific example, traded in the New York Harbor bargemarket, RBOB is a wholesale non-oxygenated blendstock that is ready forthe addition of 10% ethanol at the truck rack.

In addition to RBOB, other refined products may include gasoline meetingthe specifications of the California Air Resources Board (GARB), CARBdiesel fuel, low-sulfur and ultra-low-sulfur diesel fuel, and oxygenates(liquid hydrocarbon compounds containing oxygen), conventional gasoline,distillates, jet fuel, asphalt, petrochemicals, lubricants. Gasoline isthe largest single volume refined product sold in the United States andaccounts for almost half of national oil consumption. It is a highlydiverse market, with hundreds of wholesale distributors and thousands ofretail outlets, making it subject to intense competition and pricevolatility. These refined products, branded and unbranded, are generallymarketed on a wholesale basis in the United States and Canada through abulk and rack marketing network. Some producers may sell refinedproducts through their own network of retail and wholesale brandedoutlets.

While the wholesale price component represents the price of gasoline atthe refinery level, the rack markup component represents the cost todeliver that gas from a refinery to a retailer such as a gas station(i.e., from wholesale to rack). The retailer buys the gas at the rackprice, sometimes referred to as the rack rate, which is typically markedup by whoever is involved in the transportation and distribution chain,including terminal distribution facilities, transportation companies,etc. Sellers at each step of the distribution chain generally try to settheir target prices at a level reflecting their current costs plus amarkup.

Studies have found that the rack price and the wholesale price areclosely correlated. Thus, in some embodiments, the rack markup componentmay be determined using the wholesale price component, perhaps taking apercentage thereof as an input to the rack markup component. In someembodiments, the rack markup component may be determined usinginformation provided by an information service provider such as OilPrice Information Service (OPIS) of Rockville, Md., USA. OPIS trackswhat rack markups would be, perhaps based on historical data. Gasolineand diesel rack prices are available from OPIS for more than 360wholesale rack locations every day. In addition, over 240 citiesnationwide contain the OPIS Smart Rack which details retail gasolinelows, averages and margins.

The tax component may include all types of taxes related to a retailcommodity. In the case of petroleum products, in addition to income,severance, production, property, and other taxes, they are subject tovarious excise taxes. Specifically, in some embodiment, the taxcomponent may include federal gas tax, state and local gas taxes, andfees. The federal tax is collected in all states in addition to anystate or local taxes on gasoline sales. As an example, the federal gastax may be 18.4 cents per gallon (cpg) while the state gas tax may be 20cpg in the state of Texas.

Gasoline taxes are levied in various ways in different states. Eachstate may impose different amounts of taxes depending upon the typeand/or grade and may include different types of charges and fees. Forexample, according to data collected by the American Petroleum Institute(API) in 2005, in the state of Alabama, the state gas tax is 18 cpg forgasoline and 19 cpg for diesel, each type of tax including a 2-cpginspection fee. Some states, such as Alaska, Arizona, California,Georgia, Hawaii, Idaho, Louisiana, Massachusetts, Minnesota,Mississippi, Missouri, Nebraska, New Hampshire, North Carolina, NorthDakota, Ohio, Oregon, Rhode Island, South Carolina, South Dakota, Texas,Utah, Washington, West Virginia, Wisconsin, and Wyoming, levy a flatrate per gallon for gasoline and diesel.

Some states allow local communities to levy gasoline taxes in additionto any state taxes that might be levied. Some may charge a tax similarto a sales tax in that it applies to the monetary amount of the gasolinesold. For example, in the state of California, the state gas tax is 18cpg for gasoline and diesel, plus a 6% state sales tax, a 1.25% countytax, and additional local sales taxes. Thus, determining the amount oftax paid on one gallon of gasoline or diesel fuel purchased by aconsumer at the pump can involve numerous factors and calculations. Thetax rate may vary depending on the whether the area where the fuel ispurchased is in compliance with federal clean air standards, whether athreshold amount of revenue has been collected for the taxingjurisdiction for the fiscal year, and how much is being charged for thepre-tax price of a gallon of fuel.

The API is one exemplary source that collects motor fuel tax informationfor all 50 states and compiles a report and chart detailing changes fromthe previous update and calculating a nationwide average. Although taxrates do change, they are considered relatively stable as compared tocrude oil prices, which could fluctuate continuously, not just daily.Thus, in some embodiments, tax information obtained from the API can beparsed for use in the tax component in determining the forward retailprice for gasoline in a market having a geographic boundary.

Thus, as a simplified example, the wholesale price component can beparsed from RBOB contracts, the rack markup component can be derivedfrom data provided by the OPIS, and the tax component can be calculatedbased on information provided by the API. The retail markup component,however, is very difficult to determine as each retailer may tailor themarkup, which is often kept secret, per location (L), time period (T),and grade (G). In some embodiments, as discussed below, the retailmarkup component can be estimated, alone with the rack markup component,based on purchased wholesale gas pricing data. Current retail pricesfrom other sources may also be utilized to determine the retail markupcomponent with respect to a particular location (L), time period (T),and grade (G).

FIG. 7 is a flow diagram depicting one example embodiment of a methodfor determining the current retail price of a commodity for a specificlocation (L), time period (T), and grade (G). As described above,software code implementing embodiments of the present invention can bestored on a computer-readable medium and executed by a processor tocause the processor to perform particular functions disclosed herein.

At step 701, according to one embodiment, a first functionality operatesto estimate the total markup, which includes the rack markup and theretail markup, for gas for a specified location and grade. Thedetermination of the Estimated Total Markup (ETM) can be based on anumber of factors. For example, the ETM may be determined based onpurchased wholesale gas pricing data, where available, from oilcompanies, regional retailers, OPIS, and/or other similar sources.Additionally, the ETM may be determined by extrapolating or ‘reverseengineering’ historical differences in wholesale pricing from NYdelivery markets and other markets across the nation, but using theretail prices to begin the extrapolation. In this example, the retailmarkup is therefore the difference between the ETM and the rack markup.

As a specific example, a RBOB contract may be utilized as algorithminput on the wholesale gasoline price. FIG. 8 is a plot diagramdepicting exemplary NYMEX RBOB gasoline futures historical data. If theNY delivery markets (from which the RBOB contract is priced)historically have differed by 10% for California markets, then theCalifornia projected wholesale pricing could be adjusted by thisdifference.

According to embodiments disclosed herein, another factor in determiningthe ETM is an aggregated composite retail price for gasoline for alocation and grade, which is referred to herein as “Pricelock AggregatedComposite Retail Price (PACRP)” and which is generated by a proprietarysystem and method for determining a retail commodity price within ageographic boundary. For detailed teachings on the PACRP, readers aredirected to co-pending U.S. patent application Ser. No. 12/030,119,filed Feb. 12, 2008, entitled “SYSTEM AND METHOD OF DETERMINING A RETAILCOMMODITY PRICE WITHIN A GEOGRAPHIC BOUNDARY,” which is herebyincorporated herein as if set forth in full. Other types of aggregatedcomposite retail price for gasoline within a specific locale may also beutilized. In some embodiments, the ETM is the difference between thePACRP and the wholesale price by fuel type/grade and location.

At step 703, a second functionality operates to subtract the ETM fromthe PACRP for a specific location and a specific grade or type of fuelto generate the effective wholesale price for that particular locationand that particular grade. The output of this step is to have estimatedwholesale gas prices, by location and grade, at the current moment.

FIG. 9 is a flow diagram depicting a method for synthetically creating aforward market curve of an expected retail price for gasoline for aperiod of time within a market having a geographic boundary. Referringto the above specific example where it is desired to provide priceprotection services with respect to retail gasoline prices to customerslocated in Austin, Tex., U.S.A., such a forward market curve can beutilized to project the future retail prices for gasoline in Austinthree months from now, six months from now, a year from now, and so on.

According to one embodiment, the Estimated Forward Retail Gas Price(EFRP) can be projected, station by station, using the followingcomponents: RBOB contracts on wholesale gas by type and time period, thecurrent ETM for each location and fuel type, the market volatility andcurrent gas trend as the wholesale to retail correlation and pricechange lag can differ under various market conditions. Examples ofmarket conditions include contango and backwardation. Backwardationdescribes a market where spot or prompt prices are higher than prices inthe future—a downward sloping forward curve. It indicates that promptdemand is high. Contango is the opposite, with future prices higher thanspot prices. By way of example, at step 902, software code implementinga third functionality may be executed to estimate, for each grade (fueltype), the increase/decrease of the wholesale price over a period oftime using project wholesale pricing from futures market on NY deliverymarkets. FIG. 10 is an exemplary plot depicting the increase as well asdecrease of the estimated wholesale price over time.

Next, at step 904, software code implementing a fourth functionality maybe executed to consider the market conditions inherent in the forwardcontracts (e.g., contango or backwardation) and determine the rate ofchange between the retail and wholesale spreads over time. This step canprovide a desired granularity on the final outcome. For example, anindividual station EFRP may be calculated by applying the adjusted ETM(based on step 902) to each monthly forecasted MERC wholesale contractprice. This near final station EFRP can be further adjusted by a “markettrend” factor, depending on whether it is in a contango or abackwardated market.

At step 906, according to one embodiment, software code implementing afifth functionality may operate to determine a geographic EFRP byaggregating, station by station, EFRP monthly “strips”. The geographicEFRP (e.g., local, regional, national, etc.) may be calibrated byweighting individual EFRPs based on gas purchase volumes by fuel type.

At step 908, according to one embodiment, software code implementing asixth functionality may operate to determine additional markup whereapplicable. For example, a sensitivity analysis may be performed on theEFRP from step 906 based on world events. Furthermore, the functionalitymay be configured with an ability to “toggle” pricing based upon achoice of user-defined events such as hurricanes, war, politicalchanges. Although these events are theoretically priced into the forwardcontracts on wholesale prices, they are assumed to be low probability.However, once they occur, the price of gas can spike upward and modifypricing on forward contracts. The algorithm used to calculate effects onthe EFRP can be implemented to rely on historical analysis of actualretail price changes across the nation due to actual events and theirindividual event locations. For example, Hurricane Katrina, which struckthe United States through the Gulf of Mexico, may impact gas wholesaleprices differently than Hurricane Andrew, which struck the AtlanticCoast. Thus, software code implementing this algorithm can be a usefultool for adding sensitivities to the underlying process of creating theforward total retail gas price based on prior events. Any additionalmarkup is then added to the geographic EFRP, perhaps weighted, togenerate the estimated forward retail gas price for the specified fueltype/grade and time. In some embodiments, the specified time period mayrange from one month to six months.

In the foregoing specification, the invention has been described withreference to specific embodiments. However, one of ordinary skill in theart will appreciate that various modifications and changes can be madewithout departing from the spirit and scope of the invention disclosedherein. Accordingly, the specification and figures disclosed herein areto be regarded in an illustrative rather than a restrictive sense, andall such modifications are intended to be included within the scope ofthe disclosure as defined in the following claims and their legalequivalents.

1. A method for predicting a forward retail price of a commodity withina geographic boundary over a time period, comprising: providing acomputer having a processor and a non-transitory computer readablemedium storing a set of instructions executable to perform: assessingthe components of a retail commodity within a geographic boundary,comprising: obtaining a current retail price for a retail locationwithin the geographic boundary; determining a wholesale price associatedwith the trade of a wholesale product corresponding to the retailcommodity; determining a rack markup component associated withtransporting and distributing the retail location to the retail locationwithin the geographic boundary; obtaining a tax component associatedwith the retail location within the geographic boundary; subtracting therack markup component, the tax component, and the wholesale price fromthe current retail price to determine a retail markup componentassociated with the retail location within the geographic boundary; andcreating a forward market curve indicating forward retail price of thecommodity for a period of time within the geographic boundary,comprising: determining a market condition associated with the sale ofthe commodity over a past period of time within the geographic boundaryon a monthly basis; and calculating an estimated forward retail pricefor the location within the geographic boundary over a time period basedon the current retail price of the commodity within the geographicboundary, the estimated current wholesale price of the commodity, themarket condition associated with the sale of the commodity within thegeographic boundary.
 2. The method of claim 1, wherein the marketcondition includes contango and backwardation, wherein contangodescribes a market condition where spot prices of the commodity arelower than further prices of the commodity, and wherein backwardationdescribes a market condition where spot prices of the commodity arehigher than further prices of the commodity.
 3. The method of claim 2,further comprising adjusting the estimated forward retail price for thelocation based on a user-defined event.
 4. The method of claim 3,further comprising performing a historical analysis of actual retailprice changes due to the user-defined event.
 5. The method of claim 1,wherein a wholesale price associated with the trade of a wholesaleproduct is determined based on a futures contract.
 6. The method ofclaim 5, wherein the futures contract is traded on the New YorkMercantile Exchange (NYMEX) on that day.
 7. The method of claim 6,wherein the futures contract is a gasoline futures contract.
 8. Themethod of claim 1, wherein the estimated total markup is determined byextrapolating historical differences in wholesale prices.
 9. The methodof claim 1, wherein the rack markup component is determined using apercentage of the wholesale price component.
 10. The method of claim 1,wherein the rack markup component is determined using informationprovided by an information service provider.
 11. The method of claim 1,wherein the rack markup component and the retail markup component aredetermined by extrapolating historical differences between retail pricesand wholesale prices as an estimated total markup.
 12. A system forpredicting a forward retail price of a commodity within a geographicboundary over a time period, comprising: a computer coupled to a networkand having a processor and a non-transitory computer readable mediumstoring a set of instructions executable to perform: receiving, from aretail computer coupled to the network, a current retail price for aretail location within the geographic boundary; receiving, from awholesale market computer coupled to the network, a wholesale priceassociated with the trade of a wholesale product corresponding to theretail commodity; receiving, from an information service providercomputer, a rack markup component associated with transporting anddistributing the retail location to the retail location within thegeographic boundary; obtaining a tax component associated with theretail location within the geographic boundary; determining a retailmarkup component associated with the retail location within thegeographic boundary; determining a market condition associated with thesale of the commodity over a past period of time within the geographicboundary on a monthly basis; and calculating an estimated forward retailprice for the location within the geographic boundary over a time periodbased on the current retail price of the commodity within the geographicboundary, the estimated current wholesale price of the commodity, themarket condition associated with the sale of the commodity within thegeographic boundary, and the retail markup component associated with theretail location within the geographic boundary.
 13. The system of claim12, wherein determining a retail markup component associated with theretail location within the geographic boundary comprises subtracting thewholesale price, the rack markup component, and the tax component fromthe current retail price of the commodity within the geographicboundary.
 14. The system of claim 12, wherein the retail computer islocated at a retail location within the geographic boundary.
 15. Thesystem of claim 12, wherein the retail computer communicates with aretail location within the geographic boundary to obtain retail priceinformation.